store = {}
store['args']={'name': 'bald_mnist_865341', 'type': 'AcquisitionFunction.bald', 'seed': 865341, 'experiment_description': 'Coreset BALD vs BALD', 'acquisition_method': 'AcquisitionMethod.independent', 'available_sample_k': 1, 'num_inference_samples': 20, 'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 1024, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'target_accuracy': 0.96, 'target_num_acquired_samples': 300, 'log_interval': 20, 'dataset': 'DatasetEnum.mnist', 'initial_samples': [38043, 40091, 17418, 2094, 39879, 3133, 5011, 40683, 54379, 24287, 9849, 59305, 39508, 39356, 8758, 52579, 13655, 7636, 21562, 41329], 'experiment_task_id': 6, 'experiments_laaos': './experiment_configs/coreset_bald_vs_bald/configs.py', 'no_cuda': False, 'quickquick': False, 'initial_samples_per_class': 2}
store['cmdline']=['./src/ignite_mnist.py', '--experiment_task_id=6', '--experiments_laaos=./experiment_configs/coreset_bald_vs_bald/configs.py']
store['iterations']=[]
store['initial_samples']=[38043, 40091, 17418, 2094, 39879, 3133, 5011, 40683, 54379, 24287, 9849, 59305, 39508, 39356, 8758, 52579, 13655, 7636, 21562, 41329]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6017, 'nll': 2.8703010040283203}, 'chosen_samples': ['40487'], 'chosen_samples_score': [1.1842717240650824], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6429, 'nll': 2.396858745574951}, 'chosen_samples': ['56344'], 'chosen_samples_score': [1.2370025459826781], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6208, 'nll': 2.5515585624694825}, 'chosen_samples': ['32639'], 'chosen_samples_score': [1.2082084209035835], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6729, 'nll': 2.224877227783203}, 'chosen_samples': ['42143'], 'chosen_samples_score': [1.1726633782235365], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6943, 'nll': 2.080429906463623}, 'chosen_samples': ['29834'], 'chosen_samples_score': [1.1921785787124701], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6523, 'nll': 2.265841972732544}, 'chosen_samples': ['53056'], 'chosen_samples_score': [1.258403924740422], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6899, 'nll': 1.8988766700744628}, 'chosen_samples': ['7949'], 'chosen_samples_score': [1.186066947116168], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6806, 'nll': 1.8610159160614013}, 'chosen_samples': ['8863'], 'chosen_samples_score': [1.1017615228058064], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.687, 'nll': 1.8595089710235595}, 'chosen_samples': ['34800'], 'chosen_samples_score': [1.2560810670158862], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7009, 'nll': 1.7439766590118408}, 'chosen_samples': ['45351'], 'chosen_samples_score': [1.1097363372251343], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7121, 'nll': 1.7382464782714844}, 'chosen_samples': ['29334'], 'chosen_samples_score': [1.1570864663726752], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6981, 'nll': 1.8031860847473145}, 'chosen_samples': ['13016'], 'chosen_samples_score': [1.2063739998374325], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7026, 'nll': 1.8302288539886475}, 'chosen_samples': ['54709'], 'chosen_samples_score': [1.174208582689164], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6968, 'nll': 1.7512890201568603}, 'chosen_samples': ['27153'], 'chosen_samples_score': [1.1516712206561017], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7288, 'nll': 1.635373712158203}, 'chosen_samples': ['10757'], 'chosen_samples_score': [1.183326735504786], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7277, 'nll': 1.6949364192962646}, 'chosen_samples': ['40107'], 'chosen_samples_score': [1.1740606611815547], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7633, 'nll': 1.3691283948898316}, 'chosen_samples': ['40437'], 'chosen_samples_score': [1.2068670451055756], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.7647, 'nll': 1.6171708423614501}, 'chosen_samples': ['20794'], 'chosen_samples_score': [1.1438227825402043], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7684, 'nll': 1.3343155946731566}, 'chosen_samples': ['32327'], 'chosen_samples_score': [1.0724737325961986], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8036, 'nll': 1.116874602508545}, 'chosen_samples': ['1724'], 'chosen_samples_score': [1.1043179476413054], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7856, 'nll': 1.1846566640853882}, 'chosen_samples': ['29853'], 'chosen_samples_score': [1.0471865518880188], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8161, 'nll': 1.10653090839386}, 'chosen_samples': ['6238'], 'chosen_samples_score': [1.0525253927640246], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7766, 'nll': 1.3333721675872803}, 'chosen_samples': ['9403'], 'chosen_samples_score': [1.0428441384634102], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8111, 'nll': 1.0441455530166626}, 'chosen_samples': ['27930'], 'chosen_samples_score': [1.12406715233168], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.802, 'nll': 1.0534162912368774}, 'chosen_samples': ['25309'], 'chosen_samples_score': [0.99639083543586], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.811, 'nll': 1.106393039894104}, 'chosen_samples': ['59467'], 'chosen_samples_score': [1.0985189254629828], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8236, 'nll': 0.9954476264953613}, 'chosen_samples': ['48356'], 'chosen_samples_score': [1.0504426558632929], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8021, 'nll': 1.094306784439087}, 'chosen_samples': ['57334'], 'chosen_samples_score': [1.0799835381595666], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7978, 'nll': 1.0542575679779054}, 'chosen_samples': ['59413'], 'chosen_samples_score': [1.0316222083440274], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8137, 'nll': 0.9881078987121582}, 'chosen_samples': ['32065'], 'chosen_samples_score': [1.030875144108479], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8158, 'nll': 0.9805438194274902}, 'chosen_samples': ['49992'], 'chosen_samples_score': [0.960954783467991], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8117, 'nll': 1.0369296630859375}, 'chosen_samples': ['14866'], 'chosen_samples_score': [0.9830520128820921], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8008, 'nll': 1.0252148252487183}, 'chosen_samples': ['40108'], 'chosen_samples_score': [1.0353049376594345], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8006, 'nll': 0.9976856010437012}, 'chosen_samples': ['56212'], 'chosen_samples_score': [0.9919418906893746], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8214, 'nll': 0.9233599657058715}, 'chosen_samples': ['7033'], 'chosen_samples_score': [0.977989449222559], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8206, 'nll': 1.0184720998764039}, 'chosen_samples': ['8443'], 'chosen_samples_score': [1.099970539983341], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8345, 'nll': 0.9374573797225952}, 'chosen_samples': ['36281'], 'chosen_samples_score': [0.9822651720533241], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8013, 'nll': 0.9588735149383545}, 'chosen_samples': ['5129'], 'chosen_samples_score': [0.8820874036257113], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8178, 'nll': 0.9208670705795288}, 'chosen_samples': ['41001'], 'chosen_samples_score': [0.903152980106535], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8502, 'nll': 0.9508850841522217}, 'chosen_samples': ['46832'], 'chosen_samples_score': [1.180032525211539], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8049, 'nll': 0.971120657157898}, 'chosen_samples': ['34481'], 'chosen_samples_score': [0.949391696279836], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8043, 'nll': 0.9468213935852051}, 'chosen_samples': ['12345'], 'chosen_samples_score': [0.8791653068066159], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8207, 'nll': 0.9359870193481445}, 'chosen_samples': ['1881'], 'chosen_samples_score': [0.9441471947883698], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8174, 'nll': 0.9173727514266968}, 'chosen_samples': ['37508'], 'chosen_samples_score': [0.9023843180028827], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8569, 'nll': 0.8757140964508057}, 'chosen_samples': ['8843'], 'chosen_samples_score': [1.0785482367607369], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8677, 'nll': 0.8194830978393555}, 'chosen_samples': ['3522'], 'chosen_samples_score': [1.1149029989528152], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8146, 'nll': 0.9722617593765259}, 'chosen_samples': ['55526'], 'chosen_samples_score': [0.856205075300223], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8458, 'nll': 1.0512508380889893}, 'chosen_samples': ['38308'], 'chosen_samples_score': [1.26768182160108], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8249, 'nll': 0.8979173038482666}, 'chosen_samples': ['29380'], 'chosen_samples_score': [0.8313255485961164], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8555, 'nll': 0.8976147842407226}, 'chosen_samples': ['24513'], 'chosen_samples_score': [1.2392930765553452], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8569, 'nll': 0.9193184447288513}, 'chosen_samples': ['12663'], 'chosen_samples_score': [1.1651254516467193], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8245, 'nll': 0.8862801822662354}, 'chosen_samples': ['2352'], 'chosen_samples_score': [0.9095264113422923], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8596, 'nll': 0.86764737033844}, 'chosen_samples': ['32505'], 'chosen_samples_score': [1.1567407199328814], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8493, 'nll': 0.8866262567520141}, 'chosen_samples': ['31046'], 'chosen_samples_score': [1.1292740044077054], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.852, 'nll': 0.8888145921707153}, 'chosen_samples': ['7917'], 'chosen_samples_score': [1.0768946429528112], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8796, 'nll': 0.8097063148498536}, 'chosen_samples': ['55998'], 'chosen_samples_score': [1.175236742780992], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8859, 'nll': 0.7507025987625122}, 'chosen_samples': ['21393'], 'chosen_samples_score': [1.1367883676626875], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.874, 'nll': 0.7567963190078736}, 'chosen_samples': ['36486'], 'chosen_samples_score': [1.029759165921574], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8907, 'nll': 0.71183352394104}, 'chosen_samples': ['20035'], 'chosen_samples_score': [1.013939439338035], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9125, 'nll': 0.6225832134246826}, 'chosen_samples': ['12497'], 'chosen_samples_score': [1.0945122837418033], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8855, 'nll': 0.6892962602615357}, 'chosen_samples': ['60'], 'chosen_samples_score': [1.0964451884333968], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9021, 'nll': 0.7181668468475342}, 'chosen_samples': ['42334'], 'chosen_samples_score': [1.3920887873270262], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9023, 'nll': 0.6484363289833068}, 'chosen_samples': ['5474'], 'chosen_samples_score': [1.0381630702162394], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8989, 'nll': 0.6779782518386841}, 'chosen_samples': ['53236'], 'chosen_samples_score': [1.0827986417003452], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9158, 'nll': 0.6090027750015259}, 'chosen_samples': ['16860'], 'chosen_samples_score': [1.145641242748909], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.908, 'nll': 0.5865007164001464}, 'chosen_samples': ['39355'], 'chosen_samples_score': [1.0081444462806246], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8923, 'nll': 0.6845586853027343}, 'chosen_samples': ['38298'], 'chosen_samples_score': [1.100615977999051], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9064, 'nll': 0.5998675647735596}, 'chosen_samples': ['33197'], 'chosen_samples_score': [0.9933789937349193], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8875, 'nll': 0.7445252754211426}, 'chosen_samples': ['42228'], 'chosen_samples_score': [1.0794340284186523], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9071, 'nll': 0.6292067653656006}, 'chosen_samples': ['49064'], 'chosen_samples_score': [1.0698647449585301], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9155, 'nll': 0.6508145654678344}, 'chosen_samples': ['32276'], 'chosen_samples_score': [1.281337751539467], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8938, 'nll': 0.6580234294891357}, 'chosen_samples': ['13680'], 'chosen_samples_score': [1.0867567397579476], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9056, 'nll': 0.6454876142501831}, 'chosen_samples': ['39411'], 'chosen_samples_score': [1.1191847264293568], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9099, 'nll': 0.6254754524230957}, 'chosen_samples': ['22579'], 'chosen_samples_score': [1.178859889388201], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8992, 'nll': 0.6343577907562256}, 'chosen_samples': ['48006'], 'chosen_samples_score': [0.9729831816706652], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9143, 'nll': 0.599075122642517}, 'chosen_samples': ['36642'], 'chosen_samples_score': [1.1114886939930924], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8985, 'nll': 0.6512118900299072}, 'chosen_samples': ['35246'], 'chosen_samples_score': [0.9843325978799725], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9046, 'nll': 0.6861798887252808}, 'chosen_samples': ['35606'], 'chosen_samples_score': [1.0418394718932231], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9038, 'nll': 0.6464117234230041}, 'chosen_samples': ['23104'], 'chosen_samples_score': [0.9650991107552747], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8929, 'nll': 0.6530298663139343}, 'chosen_samples': ['30223'], 'chosen_samples_score': [0.9828171756308933], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9115, 'nll': 0.6514292716026306}, 'chosen_samples': ['7833'], 'chosen_samples_score': [1.11049701928569], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8862, 'nll': 0.6920236722946167}, 'chosen_samples': ['47322'], 'chosen_samples_score': [0.9530995773201603], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8985, 'nll': 0.6587635062217713}, 'chosen_samples': ['31706'], 'chosen_samples_score': [1.0877196536795828], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9072, 'nll': 0.6347289164543152}, 'chosen_samples': ['37161'], 'chosen_samples_score': [1.0742693379789556], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9083, 'nll': 0.6140842795372009}, 'chosen_samples': ['13742'], 'chosen_samples_score': [1.140521712848953], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9116, 'nll': 0.5902387687683105}, 'chosen_samples': ['20641'], 'chosen_samples_score': [1.0829755649859443], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9192, 'nll': 0.5679570464134216}, 'chosen_samples': ['45516'], 'chosen_samples_score': [0.9982729335058457], 'chosen_samples_orignal_score': None})
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